Recent advancements in the field of object detection have led to development of various methods and techniques for vehicle tracking in live videos. In conventional systems, a conventional electronic device may track a vehicle in a captured video by a brute force object detection approach. In the brute force object detection approach, the conventional electronic device may be configured to apply an object detection technique on each image frame of the video to detect a position of the vehicle in the respective image frame of the captured video. The conventional electronic device may be configured to track the vehicle in the captured video, based on a detection of the position of the vehicle in each image frame of the captured video. In cases where the captured video is a high definition (HD) video, the captured video may have a large number of image frames per second. In such cases, the conventional electronic device may be required to apply the object detection technique on each image frame of the captured HD video, in real time, to track the vehicle in the captured video. Application of the object detection technique on each of the large number of image frames of the captured video, in real-time or near real-time, may be a computationally resource intensive process. Further, existing automatic license plate recognition systems from a video using conventional optical character recognition or object detection techniques may be a very slow and an error prone process.
Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.